BM432 Data Visualisation

Morgan Feeney

University of Strathclyde

Leighton Pritchard

University of Strathclyde

2024-10-23

1. Introduction

Learning Objectives

  • You should be able to critically analyse how data is visualised
  • You should be able to judge a figure’s clarity and potential for misunderstanding
  • You should be able to identify potential sources of bias resulting from the visualisation
  • You should understand how to create effective figures for your own work

Background Reading

3. Exercise

Assessing four figures

  • Use the DOIs provided to find the papers the figures are from, if you need more information than is in the figure legend

  • For each figure, consider the following:

    • What type of data is being presented?
    • Are the data presented effectively? (why/why not?)
    • How might the data presentation be improved?
    • How would you grade this figure according to University Marking Guide B?

  • Fill in the pro forma with your scoring and free-text answers to the questions above (one sentence each)

Figure 1 (doi:10.1073/pnas.2320257121)

Figure 2 (doi:10.1073/pnas.2405474121)

Figure 3 (doi:10.1073/pnas.2408540121)

Figure 4 (doi:10.1073/pnas.240342112)

4. Individual Figures

Figure 1 (doi:10.1073/pnas.2320257121)

Figure 1 (doi:10.1073/pnas.2320257121)

  • Suggested improvements (LP):
    • The lower extent of error bars is not visible in (B), (D), (G), or (E). Avoid “dynamite plots.”
    • Bar charts should be avoided; 1D scatterplot for (D), (G), and (E) would be clearer. Line plot with concentration on \(x\)-axis would improve (B).
    • We can’t see difference between effects of two concentrations of A16 in (D), or A16 vs A18 in (B); use a table of contrasts.
    • Place things to be compared by the reader next to each other where possible (E).

Figure 1 (doi:10.1073/pnas.2320257121)

  • Suggested improvements (MF):

    • The scale on the micrographs (C) is too small to read easily.
    • In addition to showing DAPI and immunofluorescence images of the cells, we should really be seeing a bright-field micrograph of the cells (no fluorescence).
    • The colour scheme is misleading (compare 1 \(\mu\)M A18 in B vs 5 \(\mu\)M in D)
    • Too many comparisons in B - chartjunk
    • y-axes scales should be the same to make intra-panel comparisons easier

Figure 1 (doi:10.1073/pnas.2320257121)

Suggestions for improvement
1D scatterplots. Line graph for B.

Figure 2 (doi:10.1073/pnas.2405474121)

Figure 2 (doi:10.1073/pnas.2405474121)

  • Suggested improvements (LP):
    • UMAP plots (B, E) are highly manipulable and clustering/placement does not necessarily reflect objective measures.
    • Unpleasant colour choices in (C); there is room for aesthetic improvement.
    • The proportion plot in (C) does not give information on absolute number, only proportion; a proportional areas plot spanning all clusters would more honestly represent the data.
    • Heatmap text is too small to read comfortably; is there too much data here?

Figure 2 (doi:10.1073/pnas.2405474121)

  • Suggested improvements (MF):
    • The flow diagram (A) could make better use of arrows to illustrate order of steps
    • Text overall is too hard to read comfortably
    • Heatmap in (D) is missing a scale (is purple high and yellow low, or vice versa?)
    • Consider what is needed to convey the figure’s intended message: if it’s just that these macrophages exhibit transcriptional heterogeneity, then D is probably sufficient for that purpose - the other panels don’t add much for me
    • Whitespace usage could be improved - cramming (C) under the inset from (B) makes the figure feel very crowded

Figure 2 (doi:10.1073/pnas.2405474121)

Suggestions for improvement
Improve colour scheme in stacked bar chart. Don’t use UMAP.

Figure 3 (doi:10.1073/pnas.2408540121)

Figure 3 (doi:10.1073/pnas.2408540121)

  • Suggested improvements (LP):
    • The lower extent of error bars is not visible in (D) or (E). These are “dynamite plots,” which should be avoided.
    • Bar charts should be avoided in general; a 1D scatterplot of each dataset in (D) and (E) would be clearer.

Figure 3 (doi:10.1073/pnas.2408540121)

  • Suggested improvements (MF):
    • Failure to complement the triple mutant strain???
    • Fluorescence wavelength not specified (C)
    • Colour scheme in (A) is inconsistent
    • Axis label in (B) could be improved
    • Figure legend for (A) could be more succinct/some of this info should be in the paper text instead

Figure 3 (doi:10.1073/pnas.2408540121)

Suggestions for improvement
Use 1D scatters for D and E

Figure 4 (doi:10.1073/pnas.240342112)

Figure 4 (doi:10.1073/pnas.240342112)

  • Suggested improvements (LP):
    • The rifampicin structure is purely decorative and could be removed.
    • The lower extent of error bars is not visible in (B). This is a “dynamite plot,” which should be avoided.
    • Bar charts should be avoided in general; a 1D scatterplot of each dataset in (B) would be clearer.
    • The implied membrane in (C) and (D) could be stated as such in the figure.

Figure 4 (doi:10.1073/pnas.240342112)

  • Suggested improvements (MF):
    • Colours in (A) difficult to distinguish, especially with the red boxes which seem to skew blue closer to purple
    • The Western blot in (A) showing 2 cut-out bands is absolutely not an appropriate way to present this type of data - show the whole thing
    • Colour scheme in (B) doesn’t seem purposeful and doesn’t add anything to the figure
    • Text in (D) is too small to read easily
    • By convention in microbiology, would assume that the periplasm/extracellular space is “up” and the cytoplasm is “down” in (C) and (D) - but this should really be labelled to avoid any potential confusion

Figure 4 (doi:10.1073/pnas.240342112)

Suggestions for improvement
1D scatterplots for B

5. Summing Up

General Comments

  • Colour choices
  • Larger figures/graphs, more space between figures/graphs
  • Too much data per figure
  • Split into multiple figures
  • Remove unnecessary data (how do we define this?)
  • “The data is presented in a manner that would likely be inaccessible for people without prior experience. A move toward a more palatable/digestible format will facilitate better science communication in the future.”

Visualising Data About Data Visualisation

  • How did you explain your marks?

Visualising Data About Data Visualisation

  • What words did you use to describe ways the figure could be improved?

Data Visualisation is Not Neutral